The document provides a comprehensive overview of Social Network Analysis (SNA), emphasizing its foundational concepts, metrics, tools, and applications. SNA examines relationships within networks of individuals, groups, or organizations, utilizing methods like degree, betweenness, closeness, eigenvector, and pagerank centralities, local clustering coefficient, density, size, average degree, centralization to analyze nodes' roles and connectivity. Historical development highlights its evolution from formal sociology to a distinct interdisciplinary field supported by software tools like Gephi, UCINET, NodeXL, PAJEK, R Programming packages and NetworkX. Applications span education, healthcare, and business, with growing relevance in handling large, complex data. Current trends focus on artificial intelligence, privacy, and advanced network structures, underscoring SNA's expanding significance in academic and commercial contexts. Finally, examples demonstrating the calculation of metrics within a social network are provided.